Improvement of Evolutionary Computation Approaches for Continuous Dynamical System Identification - Robustness and Performance Improvement of Standard Genetic Programming by Approximation, Multiple Shooting Methods, and Iterative Approaches
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @PhdThesis{Buchsbaum:thesis,
-
author = "Thomas Buchsbaum",
-
title = "Improvement of Evolutionary Computation Approaches for
Continuous Dynamical System Identification - Robustness
and Performance Improvement of Standard Genetic
Programming by Approximation, Multiple Shooting
Methods, and Iterative Approaches",
-
school = "Institute of Engineering and Business Informatics,
Graz University of Technology",
-
year = "2007",
-
address = "Kopernikusgasse 24 Graz, Austria",
-
keywords = "genetic algorithms, genetic programming, System
Identification, Evolutionary Computation, Design of
Experiments, Input Signal Shaping, Multiple shooting
Approximation Time series modeling, Dynamical systems,
Continuous state space models, System Identifikation,
Evolutionaere Algorithmen, Zeitreihenanalyse,
Versuchsplanung, Eingangsgroessenoptimierung",
-
language = "English",
-
URL = "https://online.tugraz.at/tug_online/pl/ui/$ctx;lang=DE/wbAbs.showThesis?pThesisNr=24591&pOrgNr=13706",
-
URL = "https://graz.elsevierpure.com/en/publications/improvement-of-evolutionary-computation-approaches-for-continuous",
-
abstract = "The objective of a mathematical model is to describe
certain aspects of a real system; the aim of system
identification is to create models from, usually noisy,
measurement data. Genetic Programming (GP) is a
biology-inspired method for optimising structured
representations in general, and dynamical model
structures and their parameters in particular. It has
been applied to continuous dynamical system
identification, but suffered from weak performance and
premature convergence behavior. This thesis
investigates GP's suitability for creating nonlinear
continuous state-space models from noisy time series
data. Methodologies are introduced that improve GP's
performance and robustness. For the considered test
problem, it is shown that instead of solving a
dynamical problem by an initial value method, a static
problem can be approximated, which can be solved by
symbolic regression. This approximation approach speeds
up evolution considerably. Fitness evaluation using
multiple shooting methods, known from the field of
chaotic time series, simplifies the optimization
problem by smoothing the fitness landscape; the GP
algorithm finds useful building blocks more easily.
Three concepts for integrating multiple shooting into
GP systems are developed and compared. This thesis
offers a concept for automatically switching the
identification approach based on the information
content of the training data. Computational studies
showed that automatic switching combined the advantages
of different identification approaches: Better models
were created in shorter times. Further, multi-objective
methods for regularisation were shown to improve the
evolved models generalization abilities substantially.
Investigations of model-based input signal optimisation
by evolutionary computation methods complete this
dissertation. The developed methodologies improve GPs
performance and robustness on continuous dynamical
system identification tasks. This makes GP a useful
tool that assists human modelers in finding building
blocks for model synthesis. By applying the introduced
methods, the chance of finding hidden information in
complex signals can be increased. Medicine, natural
sciences, technology, and business could benefit from
the improved prediction qualities of the resulting
models and the cost savings due to data-efficient
modeling procedures.",
-
notes = "also known as
\cite{711f8ca066c0490eb8e33fadbcd363a9}",
- }
Genetic Programming entries for
Thomas Buchsbaum
Citations